手法を比較

	ロバスト・ベイズ的モデル平均	マルコフ連鎖モンテカルロ法 (MCMC)
分野	ベイズ	ベイズ
系統	Bayesian methods	Bayesian methods
提唱年	1999–2012	—
提唱者	Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others	—
種類	Bayesian model selection and averaging	Posterior sampling algorithm
原典	Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
別名	robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
関連	6	3
概要	Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observations, or when the prior on model parameters would otherwise dominate the results.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.